SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Showing 451475 of 2042 papers

TitleStatusHype
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous DrivingCode0
Probing Multimodal Large Language Models for Global and Local Semantic RepresentationsCode0
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural NetworkCode0
Putting visual object recognition in contextCode0
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object RecognitionCode0
PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable ConvolutionCode0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
Experiments with mmWave Automotive Radar Test-bedCode0
Training Deep Neural Networks via Branch-and-BoundCode0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Decision-making and control with diffractive optical networksCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural NetworksCode0
Reconstruction-guided attention improves the robustness and shape processing of neural networksCode0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
Dynamic Rectification Knowledge DistillationCode0
A Domain Guided CNN Architecture for Predicting Age from Structural Brain ImagesCode0
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
Deep Co-Occurrence Feature Learning for Visual Object RecognitionCode0
Domain Generalization In Robust Invariant RepresentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified